01bb07dba8
FocalLoRA, Should-It-Be-Executed-Or-Processed, and topicattack were nested git repos (with an inner FocalLoRA/data/FocalLoRA/.git as well). Drop their .git history and track the contents directly in this repo instead of as submodules/gitlinks. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
44 lines
1.7 KiB
Markdown
44 lines
1.7 KiB
Markdown
# TopicAttack: An Indirect Prompt Injection Attack via Topic Transition
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Official code implementation for EMNLP 2025 accepted paper: TopicAttack: An Indirect Prompt Injection Attack via Topic Transition (https://arxiv.org/pdf/2507.13686)
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### Environment
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```
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conda creat -n topicattack python=3.10
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conda activate topicattack
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pip install openai
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```
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### Chatbot Evaluation
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We have crafted the dataset with **TopicAttack** and you can find them in `data/crafted_instruction_data_squad_conversation_attack_complete.json` and `data/crafted_instruction_data_tri_conversation_attack_complete.json`.
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To evaluate the performance, you can run the following code :
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```angular2html
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python run_baselines.py \
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--victim_model_path Qwen/Qwen2-7B-Instruct \
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--data_path data/crafted_instruction_data_squad_conversation_attack_complete.json \
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--victim_system_path prompts/generator_system_prompt.txt \
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--attacks conv_attack \
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--defenses none sandwich \
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--log_file squad_logs/sandwich-conv-ignore-qwen2-7b-squad-complete.txt
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```
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You can also replace the victim model with other models such as `GPT-4o`.
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### Attack Construction
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To construct the attack data, please put your `OpenAI api key` into the `chatbot.py`. Then you can run the following code:
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```angular2html
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python construct_attack.py \
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--model gpt-4o \
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--data_path ./data/crafted_instruction_data_squad_injection_qa.json \
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--template_path prompts/attack_prompt_transfer.txt \
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--chatbot_system_path prompts/chatbot_system.txt \
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--output_path ./data/crafted_instruction_data_squad_conversation_attack_complete.json
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```
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### Agent Evaluation
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Please step into the `injectagent` directory for more details, we implement our attack based on [InjecAgent](https://github.com/uiuc-kang-lab/InjecAgent).
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